This website uses cookies primarily for visitor analytics. Certain pages will ask you to fill in contact details to receive additional information. On these pages you have the option of having the site log your details for future visits. Indicating you want the site to remember your details will place a cookie on your device. To view our full cookie policy, please click here. You can also view it at any time by going to our Contact Us page.

Deep learning algorithm trumps cardiologists

17 July 2017

A new deep learning algorithm can diagnose 14 types of heart rhythm defects better than cardiologists. This could speed diagnosis and improve treatment for those in rural areas.

Stanford computer scientists have developed an algorithm that can sift through hours of heart rhythm data, generated by wearable monitors, to find sometimes life-threatening irregular heartbeats, called arrhythmias. The algorithm performs better than trained cardiologists (see ‘Research footnote’ below), and has the added benefit of being able to sort through data from remote locations; Stanford researchers say their algorithm could bring quick, accurate diagnoses of heart arrhythmias to people without ready access to cardiologists.

The study’s graduate student and co-lead author, Awni Hannun, said: “One of the big deals about this work, in my opinion, is not just that we do abnormality detection but that we do it with high accuracy across a large number of different types of abnormalities. This is definitely something that you won’t find to this level of accuracy anywhere else.”

People suspected to have an arrhythmia will often get an electrocardiogram (ECG) in a doctor’s office. However, if an in-office ECG doesn’t reveal the problem, the doctor may prescribe the patient a wearable ECG that monitors the heart continuously for two weeks. The resulting hundreds of hours of data would then need to be inspected second by second for any indications of problematic arrhythmias, some of which are extremely difficult to differentiate from harmless heartbeat irregularities.

Researchers in the Stanford Machine Learning Group saw this as a data problem. They set out to develop a deep learning algorithm to detect 14 types of arrhythmia from ECG signals. They collaborated with the heartbeat monitor company iRhythm to collect a massive dataset that they used to train a deep neural network model. In seven months, it was able to diagnose these arrhythmias about as accurately as cardiologists and outperform them in most cases.

The researchers predict that this algorithm could someday help make cardiologist-level arrhythmia diagnosis and treatment more accessible to people who are unable to see a cardiologist in person. Research lead Andrew Ng, adjunct professor of computer science believes this is just one of many opportunities for deep learning to improve patients’ quality of care and help doctors save time.

Research footnote: to test the accuracy of this algorithm, the researchers gave a group of three expert cardiologists 300 undiagnosed clips and asked them to reach a consensus about any arrhythmias present in the recordings. Working with these annotated clips, the algorithm could then predict how those cardiologists would label every second of other ECGs with which it was presented – in essence, giving a diagnosis.

The group had six cardiologists, working individually, diagnose the same 300-clip set. The researchers then compared which more closely matched the consensus opinion – the algorithm or the cardiologists working independently. They found that the algorithm is competitive with the cardiologists, and able to outperform cardiologists on most arrhythmias.